Many modern complex systems can be represented as extended finite state machines. Computer communications protocols, computer or machine user interfaces and microprocessors are some commonly found examples of systems represented as extended finite state machines. Prior techniques for measuring extended finite state machine performance, e.g., the time it takes the machine to perform a particular service or task, have used either approximate mathematical models as the basis for performing simulations of the extended finite state machine or actual implementations of the extended finite state machine and after the fact measurements of performance. Problems with the first technique are that the results are only approximations and there are difficulties interpreting them. When comparing the performance of two different extended finite state machines that provide the same service it has been found that there generally is difficulty in normalizing the models of each machine to obtain a fair comparison. A problem with using an actual implementation is the expense in terms of time and money required before any results can be obtained. If a comparison is desired to choose the best from among several contending extended finite state machines, the cost increases since an actual implementation of each of the several extended finite state machines is required. An additional drawback with using actual implementations is that any change made to the extended finite state machines requires redoing the implementations. Another problem with using actual implementations is that the measurements that are made for use in a comparison are dependent on the details of each specific implementation, which may not yield an equitable comparison.